System and method for providing pharmaceutical data in a secure and affordable manner

ABSTRACT

A system and method for evaluating the performance of a first at least one of a plurality of outlets is provided. The system includes a computer system configured to execute a data access application, wherein the data access application includes a plurality of user accounts, wherein each of the plurality of user accounts includes a user group, and wherein a second at least one of the plurailty of outlets is associated with the user group. The system also including a data storage device configured to store market measures from a portion of the plurality of outlets and industry-standard market measures, wherein the data access application allows a user to access the industry-standard market measures and data associated with the outlets associated with the user group of user&#39;s user account.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 60/404,451 filed Aug. 19, 2002, entitled “RXISIGHTSECURITY PROCEDURES”. The entire disclosure of which is incorporatedherein by reference.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to security techniques for customeraccessible databases.

2. Background Art

Pharmacies and wholesale pharmaceutical outlets have engaged varioustools to evaluate their market share and monitor other key performanceindicators in their national or local operating areas. One approach isto access company data, e.g., dispensed prescriptions, cash pricing,managed care contract rates, and generic dispensing ratios, and tocompare that to industry standard market measures. For example, apharmacy chain might determine their own company's prescriptiondispensing growth is 3% in the Philadelphia market, while the overallprescription volume is growing at 8%, and therefore determine that thechain is losing market share.

In general, such pharmaceutical outlets treat their own marketperformance information to be a highly protected trade secret.Accordingly, there exists a need for a technique which provides a secureenvironment to provide information services to these chains, in acost-effective manner, while protecting the confidentiality of thesedata, and reducing opportunity for error or corruption in the data.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a technique forpermitting pharmaceutical outlets to evaluate their market share in asecure environment.

Another object of the present invention is to provide informationservices to pharmaceutical outlets in a cost-effective, confidentialaccurate manner.

In order to meet these and other objects of the present invention whichwill become apparent with reference to further disclosure set forthbelow, the present invention provides a database security accessmethodology, combined with a software application and sophisticatedreference files and/or database files which enable pharmacy outlets toaccess confidential pharmaceutical information related to theirrespective companies, from one single database which houses all chains'data.

The technique is cost-effective since the cost of populating, updating,and maintaining this information, housed on a central platform, can bespread across all pharmacy chains subscribing to the database. Thechains do not have to incur the costs of purchasing a hardware platformand managing it themselves. There is no time delay from when the currentmonth's information is available, and the chains' receipt of the data,and they can log on and access it immediately upon completion of thedata load at the data warehouse. There is no delay in transmitting orshipping the data to a chain's data center and then waiting for the dataload to occur there. Additionally, the data warehouse does not have togenerate multiple large data marts for each chain that desires access tothese data, thus resulting in additional cost savings, minimizing riskof data corruption in reproduction, and facilitating maintenance andupdates to the data by only having one data warehouse to update versusmany.

A system and method for evaluating the performance of a first at leastone of a plurality of outlets is provided. The system includes acomputer system configured to execute a data access application, whereinthe data access application includes a plurality of user accounts,wherein each of the plurality of user accounts includes a user group,and wherein a second at least one of the plurailty of outlets isassociated with the user group. The system also including a data storagedevice configured to store market measures from a portion of theplurality of outlets and industry-standard market measures, wherein thedata access application allows a user to access the industry-standardmarket measures and data associated with the outlets associated with theuser group of user's user account.

In another advantageous embodiment of the present invention, a systemand method for evaluating the performance of a first at least one of aplurality of outlets is provided. The system includes a computer systemconfigured to execute a data access application, wherein the data accessapplication includes a plurality of user accounts, wherein each of theplurality of user accounts includes a user group, and wherein a secondat least one of the plurailty of outlets is associated with the usergroup. The system also including a data storage device configured tostore market measures from a portion of the plurality of outlets andindustry-standard market measures, wherein the data access applicationallows a user to access the industry-standard market measures and dataassociated with the outlets associated with the user group of user'suser account.

The accompanying drawings, which are incorporated and constitute part ofthis disclosure, illustrate preferred embodiments of the invention andserve to explain the principles of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of an exemplary method in accordance withthe present invention;

FIG. 2 is a block diagram showing an arrangement of sales outlets and aprocessing station illustrative of an embodiment of the invention;

FIG. 3 is a block diagram of a processing system that may be used as thecentral station of FIG. 2;

FIG. 4 is a flow chart showing the estimation of prescription sales atan outlet that is illustrative of the invention;

FIG. 5 is a flow chart showing the determination of sampled andunsampled outlets of FIG. 4 in greater detail;

FIG. 6 is a flow chart showing the selection of the group of sampledoutlets for each unsampled outlet in greater detail;

FIG. 7 is a flow chart showing one arrangement for the estimation ofprescriptions sales for a prescribing physician at a plurality ofpharmacies illustrative of the invention;

FIG. 8 is a flow chart showing the confidence signal operation of FIG. 7in greater detail; and

FIG. 9 is a flow chart showing another arrangement for the estimation ofprescription sales for a prescribing physician at a plurality ofpharmacies illustrative of the invention.

Throughout the drawings, the same reference numerals and characters,unless otherwise stated, are used to denote like features, elements,components, or portions of the illustrated embodiments. Moreover, whilethe present invention will now be described in detail with reference tothe FIGS., it is done so in connection with the illustrativeembodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 illustrates an exemplary embodiment of a system and method forpermitting retail outlets or a chain of retail outlets to evaluate theirperformance in a particular market in a secure environment. Generally,the exemplary system and method deliver proprietary sales data andmarket sales data to a user, thus allowing the user to appreciate theperformance of the user's retail outlet or chain of retail outlets.Specifically, the exemplary methods and systems deliver proprietarypharmaceutical chain data and pharmaceutical market data to a user, thusallowing the user to appreciate the performance of the user'spharmaceutical retail outlet or chain of pharmaceutical retail outlets.

FIG. 1 illustrates a block diagram of a pharmaceutical data analysissystem 10. The data analysis system 10 includes a communications network16, the central server 120, a chain database 30, a projected database 40and a store master database 50. The pharmaceutical data analysis systme10 allows a user 12, 14 to upload market measures for a current monthand receive information pertaining to pharmaceutical retail outlet, achain of pharmaceutical retail outlets, and/or a portion of the chain ofpharmaceutical retail outlets.

In a preferred embodiment, the communications network 16 is theInternet. In another preferred embodiment, the communications network 16is a private network.

The user 12 utilizes the central station 120 by using his or her useraccount and logging into a data access application 22 through thecommunications network 16. The central station 120 is a server, whichincludes a transceiver 26. Data may be transmitted or received by thecentral station 120 through the transceiver 26. The data accessapplication 22 and a database security application 24 run on the centralstation 120. The user may use the data access application 22 andindirectly the database security application 24 to upload marketmeasures for a current month, receive information, generate reports, andthe like.

In a preferred embodiment, multiple transceivers may be utilized. Inanother preferred embodiment, the transceiver 26 is a network interfacecard.

The users 12, 14 upload market measures for a particular month on amonthly basis. The data access application 22 and the database securityapplication 24 receive the market measures from the users 12, 14 andpopulate the chain database 30, the projected database 40 and the storemaster database 50 with market measures for a current month. Each of thechain database 30, the projected database 40 and the store masterdatabase 50 are Oracle-based relational databases. The chain database 30houses market measures including all pertinent transactions submitted tothe system 10 from a particular retail establishment's retailtransaction systems, and are stored in a manner which enables eachretail establishment's data to be identifiable and retrievable. Forexample, information identifying the particular pharmacy may beincluded. The retail establishment can be a single establishment or achain of establishments.

The projected database 40 houses aggregated, projected,industry-standard market measures. The technique for storing andgenerating the aggregated, projected, industry-standard market measuresis described hereinbelow with reference to FIGS. 2-9. The projecteddatabase 40 provides the benchmark market data for retailestablishments, chains of retail establishments or portions of chains ofretail establishments to compare their performance to that which isoccurring in an overall relevant marketplace.

The store master database 50 is also updated on a monthly basis. Thestore master database 50 is a proprietary reference database, alsostored in an Oracle-based relational data table, which contains therelationships between each retail store in a geographic area, its localoperating company name called the Organization Name, and its Parentcompany. Preferably, the geographic area is a country, for example: theUnited States. The store master database 50 contains informationdescribing the many diverse relationships that exist as large corporateentities continue to acquire local retail chains and independent storesoffering the product or products being tracked, while maintaining thelocal operating business name. An example of data stored in the storemaster database 50 follows as Table I: TABLE I Store Name/AddressOrganization Name Parent Name ABC DRUGS Cutter Supply, Inc. Diamond ValuWorldwide Jewel's Crystal Mart Colorful Savings Co. Diamond ValuWorldwide Lucky's Save-a-Lot Carrot-Top Enterprises Diamond ValuWorldwide Outlet A Discount Supplies Corporation A Outlet B DiscountSupplies Corporation A Sunny's Selections Clarity Surplus Diamond ValuWorldwide

In order to provide secure access to the databases 30, 40, 50, they areaccessed by users 12, 14 at the retail chain headquarters via acommercially available web-based decision support tool: the data accessapplication 22. Since it is critical that each user 12, 14 have accessto only a portion of the data contained in the database 30 and completeaccess to the data contained in the projected database 40, severallayers of security are required.

In a preferred embodiment, the data access application 22 is provided byBusiness Objects.

Each user 12, 14 has an account including a username and password. Eachaccount is also associated with at least one parent name, for example auser account may be associated with the parent name “Diamond ValuWorldwide.” Upon establishing the new user account, the user is givenaccess to all data corresponding to a particular parent entity bydefault. If a user's account is associated with Diamond Valu Worldwide,the user may access data associated with each of the stores that arealso associated with Diamond Valu Worldwide, i.e., ABC Drugs, Jewel'sCrystal Mart, Lucky's Save-A-Lot, and Sunny's Selections. The user wouldnot be able to access data corresponding to Outlet A and Outlet B,because that data is associated with the parent “Corporation A.” Theuser's access may be restricted to a portion of the data correspondingto the parent entity. A user account may be given access to datacorresponding to all stores associated with a parent entity, all storesassociated with a particular organization of a parent entity, aparticular store or another group of stores. Using the example above,Diamond Valu Worldwide may create a new user account for Jewel's CrystalMart's manager to see only data pertaining to Jewel's Crystal Mart.

In order to realize these differing levels of security, the databasesecurity application 24 utilizes Oracle security procedures. A “usergroup” is developed, associating specific rows in the database to agiven parent name, organization name or other subset of retailestablishments. A user account associated with a particular user groupis only able to access the rows associated with the user group. If thestore master database 50 included the data as described by Table I, aDiamond user group could be developed based upon the parent name“Diamond Valu Worldwide.” A user account associated with the Diamonduser group would be able to access the rows associated with ABC Drugs,Jewel's Crystal Mart, Lucky's Save-a-lot and Sunny's Selections, but notthe rows associated with Outlet A and Outlet B. It should be noted thatall user groups are provided access to the entire projected database 40.

In a preferred embodiment, a user group may be associated with more thanone parent name.

A level of security is realized using the data access application 22.Using security procedures of the data access application 22, a new useraccount may be established which enables access to the data accessapplication 22 interface. The new user account is associated with a usergroup, which is developed as described above. In the example above, anew user account for “John Smith” could be associated with the “Diamond”user group.

A further level of security is established at the data element level.Each parent entity or organization, may submit a chain attribute datafile. The chain attribute data file is merged into the chain database 30and/or the projected database 40. This file contains chain-specificattributes, from which customized reports and analytic measures may becreated. For example, a parent entity or organization could send data tothe store master database 50, which would group stores into districts orregions, in order to generate relevant reports for their district orregional managers. The chain-specific attributes are also linked intothe database via the parent name and the user group security notedabove. These data are not visible to any user outside of the retailchain's user group.

FIG. 2 depicts an arrangement illustrating a first embodiment of theinvention in which product sales at unsampled sales outlets areestimated. In FIG. 2, there are shown an area 100, sampled sales outlets110-1, 110-2, 110-3, 110-N-1 and 110-N and unsampled sales outlets 1104and 110-5 in the area 100 and a central station 120. Each of sampledoutlets 110-1, 110-2, 110-3, 110-N-1 and 110-N may preferably be coupledvia a line of lines 130-1 through 130-N to the central station 120.

In FIG. 2, the outlets may be pharmacies or other type of retail storesor distribution establishments all of which distribute a particularproduct. The outlets are at various locations in the area 100. Whilethere are 7 outlets shown in FIG. 2 for purposes of illustration, it isto be understood that there are generally hundreds or thousands ofoutlets which are not restricted to a given area. The location of eachoutlet is generally known in terms of latitude and longitude fromavailable census data or in terms of zip code centroids from Post Officedata. Accordingly, the distances between pharmacies can be determined.Product sales data generated at each outlet S_(n) (e.g., 110-1) ispreferably transferred to the central station 120 via a line (e.g.,130-1). Unsampled outlets U_(n) (e.g., 110-4 or 110-5) in the area 100are not coupled to the central station or, if coupled, do not supplyvalid sales data so that only an estimate of the sales volume of theparticular product can be made.

A single area 100 is shown in FIG. 2 for purposes of illustration only.According to the invention, the estimation of sales activity at anunsampled outlet U is formed on the basis of the sales activity at thesampled outlets S in a neighborhood of the unsampled outlet U. Theneighborhood of an unsampled outlet U may be defined as the N closestsampled outlets S which is different for each unsampled outlet and notas a predefined geographic area. In an urban area, the neighborhood ofclosest sampled outlets may all be located within a short distance ofthe unsampled outlet. In a rural area, the neighborhood of N closestsampled outlets nay spread over distances. Consequently, each unsampledoutlet has its own neighborhood area which varies according to thedistances to the nearest N sampled outlets. Advantageously, thecorrelation of sales activity data is not restricted to a predeterminedgeographic area as in the prior art.

FIG. 3 depicts a block diagram of the central station 120 of FIG. 2which includes an input/output unit 201, a characteristics and locationstore 205, a sales data store 210, a processor 215, a program store 220,a bus 225, a transfer store 228 and work station processors 230-1through 230-N. Input/output 201 is coupled to bus 225 and is alsocoupled to the sampled outlet lines 130-1 to 130-N and to output line245. The characteristics and location store 205, the prescription datastore 210 and the program store 225 are coupled to the bus 225. TheProcessor 215 is coupled to the bus 220, the transfer store 228 and isalso coupled to work station processors via a control line 240. Thetransfer store 228 is coupled to work station processors 230-1 to 230-Nand the work station processors are coupled together through a network250. Characteristics signals stored in store 205 may include signalsrepresenting the type of outlet or the total sales of all products atthe outlet. This type of data is available from sources such as DrugDistribution Data (DDD®) available from IMS America, Plymouth Meeting,Pa. DDD® is a trademark of IMS America. Program store 220 storesinstruction signals that control the operation of the processor 215 andprovide parameter signals to determine the operation of work stationprocessors 230-1 through 230-N through the processor 215 and controlline 240.

The sales outlet data received by input/output 201 from sales outletswhich may exceed 2×10⁹ records each having between 88 and 1000 bytes istransferred to data store 210. In view of the large amount of data to beprocessed, the processing is divided between the processor 215 and thework station processors 230-1 through 230-N. Information signal arraysproduced in the processor 215 are transferred to work station processors230-1 through 230-N through the transfer store 228. Each informationsignal array from the processor 215 placed in the transfer store 228 isdivided into N portions. A preassigned portion of the information signalarray in the transfer store is supplied to each of work stationprocessors 230-1 through 230-N and the processing of the portions in thework stations 230-1 through 230-N is controlled by signals from theprocessor 215 via the control line 240. After the processing of theinformation signal array portions in the work station processors, theprocessed information signal array portions are merged into oneprocessed information signal array which is returned to transfer store228 from the work station processors. The returned information signalarray in the transfer store 228 is then further processed in theprocessor 215 to produce estimate sales volume results. The operation ofthe system of FIG. 3 will be further described in connection with theestimation arrangements shown in FIGS. 4-8.

FIG. 4 depicts a flow chart illustrating the operation of the centralstation 120 of FIG. 2 in estimating the volume of sales of a particularproduct at an unsampled outlet such as outlet O4 or outlet O5 in FIG. 2.The operations depicted in the flow chart of FIG. 4 are performed byprocessor 215 and work station processors 230-1 to 230-N of FIG. 3 undercontrol of instruction signals from in the program store 220. In theflow chart of FIG. 4, product data from outlets O1, O2, O3, ON-1 and ONare transferred to the input/output unit 201 preferably viacorresponding lines 130-1 through 130-N in step 301. Transferred data isstored in outlet sales data store 210. A data transfer from an outletmay occur for each sales transaction or may include a number oftransactions for a prescribed period of time. At preset intervals, thesales data is sent to processor 215 and therein is evaluated in step 310to determine the sampled outlets Si and the unsampled outlets Up in theprocessor 215. Unsampled outlets may include outlets transferring dataevaluated as invalid.

FIG. 5 shows a method of determining sampled and unsampled outlets ingreater detail. Referring to FIG. 5, an outlet index n for the outletsO1, O2, . . . , ON is set to 1 in step 401. A sampled outlet index i andan unsampled outlet index p are set to 1 in steps 405 and 410. The salesdata for the particular product from each outlet On is checked indecision step 415 to determine if the data is valid (i.e., meetspredetermined criteria). If the data is judged to be valid in step 415,the outlet On is classified as a sampled outlet Si in step 420 and theindex i is incremented in step 425. When no data is available for theoutlet On or the data is not accepted as valid in step 415, the outletOn is classified as an unsampled outlet Up in step 422 and the index pis incremented in step 427. The index n is then incremented in step 430.Until index n is greater than N for the last outlet ON, step 415 isreentered from decision step 435. When all of the outlets 01 through ONhave been classified as sampled outlets and unsampled outlets, the lastvalue of index p (pmax) and the last value of index i (imax)representing the number of unsampled outlets and the number of sampledoutlets are stored in data store 210 (step 440).

As shown in FIG. 2, there are five sampled outlets 110-1 (O1), 110-2(O2), 110-3 (O3), 110-N-1 (ON-1) and 110-N (ON) which are designated S1,S2, S3, S4 and S5 from the processing of FIG. 5 and two unsampledoutlets 110-4 (O4) and 110-5 (O5) which are designated as U1 and U2 fromprocessing of FIG. 5. Unsampled outlet U1 is located in the centralportion of the area 100 and is surrounded by sampled outlets S1 throughS5. Unsampled outlet U2 is located at one edge of the area 100, isclosest to sampled outlet S5 and most remote from sampled outlet S1.Priorly known techniques based an estimate of the sales volume of aproduct at an unsampled outlet on the sales volume of the product forthe geographic area. Since the sales outlets have differentcharacteristics (e.g., size and location) and have sales related tooutlets outside a particular area, estimates based on the overall salesvolume in a particular area as in the prior art are biased. Inaccordance with the invention, an estimate of sales volume of aparticular product at a sales outlet is based on the known sales volumeof other outlets according to the distances between the sales outlet andthe other outlets and the particular characteristics of the outletsindependent of any geographic area. By using the outlet characteristicsand the distances, an unbiased and more accurate estimate may bedetermined.

Signals corresponding to the distances between unsampled outlet U1 andsampled outlets S1 through S5 and the distances between outlet U2 andsampled outlets S1 through S5 are then formed in step 330. In step 335,the mmax closest sampled outlets to unsampled outlet p are selected. Theselection is performed in the processor 215. mmax may be chosenaccording to the total number of sampled outlets. The selection ofsampled outlets associated with each unsampled outlet is shown ingreater detail in FIG. 6.

With reference to FIG. 6, a set of distance signals dip′ for sorting isgenerated in step 501 corresponding to the distance signals dipgenerated in step 330. The unsampled outlet index p is set to one instep 505. A selected outlet index m is set to one in step 510 and thesampled outlet index i is set to one in step 515. In step 520, a signalD is set to LPN (largest possible number) and the loop including steps525, 530, 535 and 540 is entered to find the smallest distance of thedistances dip′.

In decision step 525, the signal dip′ representing the distance fromsampled outlet Si and unsampled outlet Up is compared to D. When dip′ isless than D, D is set to dip′, Rm representing a tentative selectedoutlet is set to Si, the index i* is set to i and a tentative selecteddistance signal dmp is set to dip′ in step 530. Step 535 is then enteredin which sampled outlet index i is incremented. Where dip′ is not lessthan D, step 535 is entered directly from decision step 525. Decisionstep 540 is then entered. Until sampled outlet index i exceeds imax instep 540, step 525 is reentered to compare the next distance signal dip′to the last determined minimum distance signal. When i exceeds imax, theminimum of the selected sampled outlets is chosen as Rm. The minimumdistance signal dip′ is then set to LPN in step 545 to exclude Si* fromcomparison in step 525 and the selected outlet index m is incremented instep 550.

Step 515 is reentered from step 555 until mmax closest outlets forunsampled outlet p are selected and another outlet Rm is chosen in theloop from step 525 through 540. Upon selection of mmax sampled outlets,the unsampled outlet index p is incremented in step 560 and step 510 isreentered via decision step 565 so that a set of m sampled outlets maybe selected for the next unsampled outlet Up in FIG. 6 via decision step565. When p is greater than pmax, control is passed to step 340 in FIG.4.

In step 340, index p is set to one. A weighting factor w_(m) is thendetermined for each selected sampled outlet Rm of unsampled outlet Up instep 345. Weighting factor generation is performed in the processor 215.The weighting factor is an inverse function of the distance between thesampled outlet Rm and the unsampled outlet Up and the characteristics ofthe sampled and unsampled outlets according tow _(m)={(1/d _(RmUp) ^(q))/(Σ(T _(m) /d _(RmUp) ^(q))}*T _(Up)  (1)

where d_(RmUp) is the distance between sampled outlet S_(m) andunsampled outlet Up, the summation is over all sampled outlets for m=1to mmax, T_(up) is the unsampled outlet characteristic (e.g., totalsales volume for all products), T_(m) is the sampled outletcharacteristic and q is greater than zero. q may, for example, be 2.Index p is incremented in step 348 and control is passed to step 345until p is greater than pmax in decision step 350.

The weighting factor signals for unsampled outlets Up and the productdata for the outlets are read into the transfer store 228 as a dataarray which is divided therein into N data array portions. The processor215 sends control signals to work station processors 230-1 through 230-Nto initiate processing of the data file portions in the work stationprocessor. Each work station processor then proceeds to form a productestimate signal for the data file portion assigned to it as indicatedwith respect to the entire data file in steps 355 through 370 in FIG. 4.In step 355-1, a starting value of the unsampled outlet index p=1 isset. The loop from step 360-1 to step 370-1 is then entered. Theestimated sales of the particular product is then generated for a rangeof unsampled stores Up in step 360 according toEst(V _(Up))=Σw _(m) V _(m)  (2)

where V_(m) is the sales volume of the particular product at sampledoutlet m and the summation is over the sampled outlets from m=1 tom=mmax. The unsampled outlet index p is incremented in step 365-1 andcontrol is passed back to step 360-1 via decision step 370-1 until p isgreater than the maximum of the range processed in work stationprocessor 230-1 and an estimate of sales volume for all unsampledoutlets in the range has been formed. The processing of the other workstation processors 230-2 through 230-N is the same as described withrespect to the work station processor 230-1 except that the range isdetermined by the portion of the data file sent to the work station. Theprocessing in the work station processor 230-N is shown in the steps355-N through 370-N.

For purposes of illustration with respect to FIG. 2, the number ofselected sampled outlets mmax is chosen as 3. It is to be understood,however, that other values may be chosen. For example, if there are 50or more sampled outlets, mmax=10 is a suitable value. In FIG. 2, sampledoutlets O1, O2 and O3 are selected as the three closest sampled outletsR1, R2 and R3 to unsampled outlet U1. To illustrate the invention,assume that the distance d_(R1U1) from sampled outlet R1 to unsampledoutlet U1 is 0.4 miles, the distance d_(R2U1) between sampled outlet R2and unsampled outlet U1 is 0.2 miles and the distance d_(R3u1) betweensampled outlet R3 and unsampled outlet U1 is 0.6 miles. Further assumethat the total sales volume for all products at sampled outlets R1, R2,R3 and U1 are $3,000, $2,000, $5,000 and $4,000, respectively. Theweighting factor for sampled outlet R2 is thenw ₂={(1/0.2)²/(2000/(0.2)²+3000/(0.4)²+5000/(0.6)²)}*4000w₂=1.210084  (3)Similarly, w₁=0.302521 and w₃=0.13445377. For a sales volume of theparticular product at R1, R2 and R3 of 5, 20 and 4, respectively, theestimated sales volume of the particular product at unsampled outlet U1isEst(V _(U1))=w ₁ *v _(R1) +w ₂ V _(R2) +w ₃ v _(R3)Est(V _(u1))=26.252  (4)The product volume signals for the sampled outlets Si is then formed instep 372-1 through 372-N and the total estimated sales volume of theproduct for unsampled and the sampled outlets is then formed for eachrange in the work station processors 230-1 through 230-N in steps 375.The resulting unsampled outlet estimate and total volume estimatesignals of the processing in the work station processors is then mergedin and totaled step 380 into a result data file. The result data file istransferred to transfer store 228 and therefrom to data store 210. Theresults are then sent to output line 245 of the input/output 201.

FIG. 7 shows a flow chart illustrating estimation of the distribution ofa controlled product by a control authority. More particularly, FIG. 7shows the operation of the arrangement of FIGS. 2 and 3 in estimatingsales of a prescription product for a prescribing physician at thepharmacies. The operations in the flow chart of FIG. 7 are performed byprocessor 215 and work station processors 230-1 through 230-N of FIG. 3under control of corresponding instruction signals stored in the programstore 220. Referring to FIG. 7, location data, data of typecharacteristics of pharmacy outlets 01 through ON in FIG. 2 andphysician identification data are stored in the characteristics andlocations store 205 of FIG. 3 in step 601. Prescription data istransferred from pharmacies O1, O2, O3, ON-1 and ON and is stored inpharmacy outlet data store 210 according to the prescribing physicians(step 603).

At prescribed intervals, the total sales for the prescribing physician jof a particular prescription product is estimated in steps 605 through670 of FIG. 7. In step 605, the processor 215 operates to determine thesampled pharmacy outlets Sij and the unsampled pharmacy outlets Up for aparticular prescription product according to the validity and volume ofthe transferred prescription data of the prescribing physician j. Thearrangement shown in FIG. 5 may be used in the determination of step605. As described with respect to FIGS. 2 and 3, pharmacy outlets O1,O2, O3, ON-1 and ON can be determined as sampled outlets S1 j, S2 j, S3j, S4 j and S5 j where the sampled data is validated. Outlets O4 and O5are classified as unsampled outlets U1 and U2.

Step 610 is entered from step 605 and signals representative of thedistances dip between each sampled outlet Sij and each unsampled outletUp are generated in the processor 215. After the distance determinationof step 610, the set of nearest sampled pharmacy outlets Rmj for eachunsampled pharmacy outlet Up is selected by processor 215 according tostep 615. Selection of sampled pharmacies may be performed as describedwith respect to FIG. 6. Then, the unsampled pharmacy outlet index p isset to 1 in step 620 and a weighting factor signal wm for the eachsampled pharmacy outlet Rm (m—1 to mmax) is generated in loop from step625 to step 634. Each weighting factor signal is formed according tow _(m)={(1/d _(RmUp) ^(q))/(Σ(T _(Rm) /d _(RmUp) ^(q))}*T _(Up)  (5)where q is greater than 0, T_(Up) is the total sales volume of allproducts at pharmacy outlet Up, T_(Rm) is the total sales volume of allproducts at pharmacy outlet Rm and the summation Σis from m=1 to mmax.

After the weighting factor signals have been formed for the lastunsampled pharmacy in the loop from step 625 to 634, a data arrayincluding the weighting information and the sales data from store 210 isformed by the processor 215 and sent to transfer store 228. The dataarray is divided into N portions each of which is processed by one ofwork station processors 230-1 through 230-N to form a signalrepresenting an estimate of the total prescription product sales volumefor physician j. The operations of the work station processors arecontrolled by the processor 215 through the control line 245. Each workstation processor operates to process a predetermined range of the dataarray in the transfer store 228.

The work station processor 230-1 operates according to steps 638-1through 655-1 to form the volume product signals for physician j in therange p=1 to p=p1. In step 640-1, the prescription estimate signal forunsampled outlets Upj is formed for each unsampled pharmacy in the rangefrom p=1 to p=p1 and the sales volume signal for the sampled pharmaciesin this range is determined by the work station processor 230-1according to step 655-1. Work station processor 230-N operates insimilar manner for physician j over the range p=pN to p=pmax asindicated in FIG. 7 according to steps 638-N through 655-N. Signals aretransferred from one work station processor to another as required forthe operation of the one work station processor through the network 255.The results of the operation of work station processors 230-1 through230-N are merged in step 660 and an estimate of the prescription productvolumeV _(T)=Σ₁ V _(Sij)+Σ₂ V _(jUp)  (6)is generated in step 665 whereV _(jUp)=Σ₃{{(1/d _(RmUp) ^(q))/(Σ₃(T _(Rm) /d _(RmUp) ^(q))}*T _(Up) {V_(Rmj)  (7)Σ₁ is the summation over all sampled outlets, Σ₂ is the summation overall unsampled outlets, Σ₃ is the summation over all sampled outlets inthe neighborhood of unsampled outlet Up. At this time, a confidencesignal that estimates the degree of possible error of the total volumeV_(Tj) of the product prescriptions of the prescribing physician j isthen generated in step 670.

FIG. 8 shows the confidence signal generation operation of step 670 ingreater detail. Referring to FIG. 8, a mean squared error signal MSE isfirst generated by bootstrapping on the basis of the sales data from thesampled pharmacy outlets S1 through Simax in step 701. The bootstrappingmethod is well known in the art and is described in “The Jacknife, TheBootstrap, and Other Resampling Plans” by B. Efron, Society forIndustrial and Applied Mathematics (SIAM) Publications, Philadelphia1982.

In the bootstrapping, subsets of pharmacy outlets are selected and theprescribing physician's prescription volume is estimated therefrom. Thevariances of the “bootstrapped” estimates closely approximates the truevariance. A generalized variance function (GVF) is derived from the MSEsgenerated in step 701 of the formlog(SQRT(MSE))=a+b log(T _(j))+c log(N _(j))+d log(N _(Sj))  (8)where SQRT is the square root, a, b, c and d are regressioncoefficients, T_(j) is the estimated total of the prescription productprescribed by physician j, N_(j) is the total of prescription productsprescribed by physician j and dispensed at the sampled pharmacy outletsand N_(Sj) is the number of sampled pharmacy outlets with prescriptionproduct sales for physician j. The generalized variance function isdescribed in “Introduction to Variance Estimation” by K. M. Wolter,Springer-Verlag, New York 1985.

The values of T_(j), N_(j) and NS_(j) are determined in steps 710, 715and 720 from the prescription data in store 210 of FIG. 3 in processor215. Regression coefficient signals a, b, c and d are generated bymultiple regression techniques well known in the art and alog(SQRT(MSE)) value for the physician j is determined in step 720.Decision step 725 is then entered in which the value log(SQRT(MSE)) iscompared to K1. If log(SQRT(MSE)) is less than K1, a low estimated errorsignal is produced in step 730. The value log(SQRT(MSE)) is thencompared to K2>K1 in step 735 to produce a medium estimated error signalin step 740 if log(SQRT(MSE)) is less than K2. Where log(SQRT(MSE)) isnot less than K2, a high estimated error signal is generated in step745. While three values for the estimated error signal are determined inthe flow chart of FIG. 8, it is to be understood that any number ofvalues such of 5 may be used.

The flow chart of FIG. 9 illustrates another arrangement for estimatingthe prescription product sales volume of a prescribing physician.According to the arrangement of FIG. 9, a group of sampled pharmacies isselected for each unsampled pharmacy and a weighting factor for eachsampled pharmacy in the neighborhood of one of the unsampled pharmaciesis generated as in FIG. 7. The weighting factors for the sampledpharmacies are combined with the actual sales data for the sampledpharmacies according toV _(Tj)=Σ₁ V _(Sij)+Σ₂Σ₃ w _(sp) V _(Sij)  (9)which corresponds toV _(Tj)=ΣΣ₁ V _(Sij)[1+Σ₄ w _(ip)]  (10)where V_(Sij) is the prescription product sales volume for physician jat pharmacy i, w_(ip) is the weighting factor for a sampled pharmacy iin the selected neighborhood of unsampled pharmacy p, Σ₁ is thesummation over all sampled pharmacies and Σ₂ is the summation over allunsampled pharmacies, Σ₃ is the summation of all sampled pharmacies inthe neighborhood of unsampled pharmacy p and Σ₄ is the summation ofweighting factors associated with sampled pharmacy i. The resultingestimate of sales volume for the prescribing physician is similar tothat described with respect to FIG. 7 but the efficiency of the estimategeneration is improved.

In the method of FIG. 9, the sampled pharmacies in the neighborhood ofeach sampled pharmacy are first determined and the weighting factorsignals w_(ip) for the neighborhood sampled pharmacies are formed in theprocessor 215 of FIG. 3 on the basis of the pharmacy location, thecharacteristics data and the physician identification data in store 205.The sales data from the sampled pharmacies and the weighting factorsignals w_(ip) are transferred to the transfer store 228, dividedtherein into prescribed ranges and each range of data is supplied to anassigned one of the work station processor 230-1 through 230-N. In eachwork station processor, the estimated sales volume for each sampledpharmacy outlet in range processed by the work station processor isgenerated from the sales data and the projection factor for the sampledpharmacy outlet. The total estimated sales volume for the sampledpharmacy outlets over range of the work station processor is thengenerated. When the work station processing is completed the resultingestimated sales volumes from the work station processors are merged andtransferred via the transfer store 228 to the processor 215.

Referring to FIG. 9, the locations, type characteristics of pharmacyoutlets 01 through ON in FIG. 2 and physician identification data arestored in the characteristics and locations store 205 of FIG. 3 in step801. Prescription data is received from the pharmacies O1, O2, O3, ON-1and ON and is stored in data store 210 according to the prescribingphysicians (step 803).

The total sales for the prescribing physician j of a particularprescription product is estimated in steps 805 through 870. In step 805,the processor 215 operates to identify the sampled pharmacy outlets Sijand the unsampled pharmacy outlets Up for a particular prescriptionproduct according to the validity and volume of the transferredprescription data of the prescribing physician j. The arrangement shownin FIG. 5 may be used. As described with respect to FIGS. 2 and 4,pharmacy outlets O1, O2, O3, ON-1 and ON can be determined as sampledoutlets S1 j, S2 j, S3 j, S4 j and S5 j where the sampled data isvalidated. Outlets O4 and O5 are classified as unsampled outlets U1 andU2.

Step 810 is entered from step 805 and signals representative of thedistances dip between each sampled outlet Sij and each unsampled outletUp are generated in the processor 215. After the distance determinationof step 810, the set of nearest sampled pharmacy outlets Rmj for eachunsampled pharmacy outlet Up is selected by processor 215 according tostep 815. The selection of sampled pharmacies Rmj may be performed aspreviously described with respect to FIG. 6.

The unsampled pharmacy index p is reset to one in step 820 and weightingsignals wmp are formed for the selected sampled pharmacies Rmi in theloop from step 825 to 834. In step 825, the weighting signalw _(mp)={(1/d _(RmUp) ^(q))/(Σ(T _(m) /d _(RmUp) ^(q))}*T_(Up)  (11)is formed for each sampled pharmacy m selected as in the neighborhood ofsampled pharmacy Up. After the weighting signals for all unsampledpharmacies are formed in the processor 215, the sales data stored instore 210 and the weighting signals are transferred to transfer store228 wherein the data and weighting signal array is divided into Nportions and each portion is transferred to one of work stationprocessors 230-1 through 230-N. The processing of the portiontransferred to work station processor 230-1 is shown from step 838-1through 855-1 in FIG. 9 and the processing the portion transferred towork station processor 230-N is indicated from step 838-1 to 838-N.

With respect to the processing of the range of sampled pharmacies inwork station processor 230-1, the sampled pharmacy index i is set to onein step 838-1 and the loop from step 840-1 through 850-1 is iterated toform the estimated prescription volume V′_(ij) for the sampledpharmacies in the range from i=1 to i=i1. In each iteration, anestimated volume signal is generated for sampled pharmacy i in step 840according toVT _(Sij) =V _(Sij)[1+Σw _(mp)]  (12)where V_(Sij) is the actual prescription product sales volume forphysician j at pharmacy i, w_(mp) are the weighting factor for sampledpharmacy Sij and Σ is the summation over all weighting factorsassociated with the pharmacy Sij. [1+Σw_(mp)] is a projection factor fora physician's prescription at the sampled pharmacy V_(Sij).

After the estimated volume signal is formed for the range i=1 to i1 inthe work station processor 230-1, step 855-1 is entered from step 850-1wherein the total volume for the range from i=1 to i=i1 by summing theestimated volumes for the pharmacies Sij. The work station processor230-N operates in similar manner from step 838-N to 855-N to generate atotal estimated volume for the range from iN to imax. Work stationprocessors are interconnected by the network 250 such as an ethernet ortoken ring arrangement so that signals from one work station processorthat are required for the formation of the VT_(TSij) signal in anotherwork station are transferred. The total volume signals for the rangesare merged in step 860 and the merged signals are transferred to theprocessor 215 via the transfer store 228. The resulting estimated totalvolume signal V_(Tj) for the physician j is then formed in the processor215 (step 865) and a confidence signal for the estimated total volumeV_(Tj) is generated in step 870 as described with respect to the flowchart of FIG. 8.

The foregoing merely illustrates the principles of the invention.Various modifications and alterations to the described embodiments willbe apparent to those skilled in the art in view of the teachings herein.It will thus be appreciated that those skilled in the art will be ableto devise numerous techniques which, although not explicitly describedherein, embody the principles of the invention and are thus within thespirit and scope of the invention.

1. A data analysis system for evaluating the performance of a first atleast one of a plurality of outlets, comprising: a computer systemincluding a transceiver and configured to execute a data accessapplication, wherein the data access application includes a plurality ofuser accounts, wherein each of the plurality of user accounts includes auser group, and wherein a second at least one of the plurailty ofoutlets is associated with the user group; and a data storage devicecoupled to the computer system and configured to store market measuresfrom a portion of the plurality of outlets and industry-standard marketmeasures, wherein the data access application allows a user to accessthe industry-standard market measures and data associated with theoutlets associated with the user group of user's user account.
 2. Thedata analysis system of claim 1, wherein at least one of the pluralityof outlets is a retail sales location.
 3. The data analysis system ofclaim 1, wherein at least one of the plurality of outlets is apharmaceutical sales location.
 4. The data analysis system of claim 1,wherein the computer system receives market measures from the portion ofthe plurality of outlets through the transceiver.
 5. The data analysissystem of claim 4, wherein the computer system receives the marketmeasures from users utilizing the data access application.
 6. The dataanalysis system of claim 4, wherein the computer system generatesindustry-standard market measures based at least in part on the marketmeasures from the portion of the plurality of outlets.
 7. The dataanalysis system of claim 1, further comprising an additional datastorage unit coupled to the computer system and including a plurality ofdata records, wherein each of the data records includes an outlet name,an organization name, and a parent name.
 8. The data analysis system ofclaim 7, wherein the user group is associated with at least one of theplurality of outlets associated with a particular parent name.
 9. Thedata analysis system of claim 7, wherein the user group is associatedwith at least one of the plurality of outlets associated with aparticular organization name.
 10. The data analysis system of claim 1,wherein the first at least one of the plurality of outlets is the sameas the second at least one of the plurality of outlets.
 11. A method forevaluating the performance of at least one of a plurality of outlets,comprising the steps of: (a) receiving a username and password from auser, (b) verifying that the username and password correspond to a useraccount, wherein the user account includes a reference to a user group;(c) receiving a request for a report comparing data associated with atleast one outlet associated with the user group to industry-standardmarket measures; and (d) transmitting the report to the user.
 12. Themethod of claim 11, further comprising the step of: (e) receiving marketmeasures corresponding to one of the at least one outlet associated withthe user group.
 13. The method of claim 12, wherein the market measuresinclude all pertinent transactions from the one of the at least oneoutlet.
 14. The method of claim 11, wherein at least one of theplurality of outlets is a retail sales location.
 15. The method of claim11, wherein at least one of the plurality of outlets is a pharmaceuticalsales location.
 16. The method of claim 11, further comprising the stepof: (f) receiving market measures from the portion of the plurality ofoutlets.
 17. The method of claim 16, further comprising the step of: (g)generating industry-standard market measures based at least in part onthe market measures received from the portion of the plurality ofoutlets.
 18. The method of claim 11, further comprising the step of: (h)receiving data describing at least one of the plurailty of outlets,wherein the data describing each of the at least one of the plurality ofoutlets includes an outlet name, an organization name, and a parentname.
 19. The method of claim 18, further comprising the step of: (i)storing a plurality of data records following the step (h), wherein eachof the plurality of data records includes an outlet name, anorganization name, and a parent name.
 20. The method of claim 18,wherein the user group is associated with at least one of the pluralityof outlets associated with a particular parent name.
 21. The method ofclaim 18, wherein the user group is associated with at least one of theplurality of outlets associated with a particular organization name.